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Record W4366816090 · doi:10.7326/m23-0757

Infectious Diseases: What You May Have Missed in 2022

2023· article· en· W4366816090 on OpenAlexaff
Rand Al Ohaly, Marie-Ève Benoit, Mindy G. Schuster

Bibliographic record

VenueAnnals of Internal Medicine · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicToxin Mechanisms and Immunotoxins
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIntensive care medicineInfectious disease (medical specialty)Antimicrobial stewardshipDiseaseEpidemiologyTuberculosisImmunologyAntibioticsPopulationAntibiotic resistanceInternal medicinePathology

Abstract

fetched live from OpenAlex

In 2022, COVID-19 remained the infectious disease at the top of most internal medicine physicians' minds. However, it was not the only infectious disease that was the topic of clinically relevant research that year. This article highlights some important infectious disease evidence unrelated to COVID-19 that was published in 2022. The literature was screened for sound new evidence relevant to internal medicine specialists and subspecialists whose focus of practice is not infectious diseases. The publications highlighted relate to various organisms in different patient populations. One article provides insight into the role of Helicobacter pylori eradication in the treatment of functional dyspepsia. The descriptive epidemiology of bacterial (Staphylococcus aureus) and viral (mpox) infections are the focus of 2 other articles. Several articles address the management of resistant and difficult-to-treat infections: multidrug-resistant gram-negative infections, resistant HIV-1, rifampin-resistant tuberculosis, cryptococcal meningitis, and invasive fungal infection in the setting of neutropenia. Another article provides data on effective HIV preexposure prophylaxis in women, an understudied population. Finally, given the urgent need to reduce inappropriate use of antibiotics, an article on antibiotic stewardship for hospitalized patients with presumed sepsis in a non–intensive care unit setting is also included.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.312
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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